In the code below, I import a saved sparse numpy matrix, created with python, densify it, add a masking, batchnorm and dense ouptput layer to a many to one SimpleRNN. The keras sequential model works fine, however, I am unable to use shap. This is run in Jupyter lab from Winpython 3830 on a Windows 10 desktop. The X matrix has a shape of (4754, 500, 64): 4754 examples with 500 timesteps and 64 variables. I've created a function to simulate the data so the code can be tested. The simulated data returns the same error.
from sklearn.model_selection import train_test_split import tensorflow as tf from tensorflow.keras.models import Sequential import tensorflow.keras.backend as Kb from tensorflow.keras import layers from tensorflow.keras.layers import BatchNormalization from tensorflow import keras as K import numpy as np import shap import random def create_x(): dims = [10,500,64] data = [] y = [] for i in range(dims[0]): data.append([]) for j in range(dims[1]): data[i].append([]) for k in range(dims[2]): isnp = random.random() if isnp > .2: data[i][j].append(np.nan) else: data[i][j].append(random.random()) if isnp > .5: y.append(0) else: y.append(1) return np.asarray(data), np.asarray(y) def first_valid(arr, axis, invalid_val=0): #return the 2nd index of 3 for the first non np.nan on the 3rd axis mask = np.invert(np.isnan(arr)) return np.where(mask.any(axis=axis), mask.argmax(axis=axis), invalid_val) def densify_np(X): X_copy = np.empty_like (X) X_copy[:] = X #loop over the first index for i in range(len(X_copy)): old_row = [] #get the 2nd index of the first valid value for each 3rd index indices = first_valid(X_copy[i,:,:],axis=0, invalid_val=0) for j in range(len(indices)): if np.isnan(X_copy[i,indices[j],j]): old_row.append(0) else: old_row.append(X_copy[i,indices[j],j]) X_copy[i,0,:]= old_row for k in range(1,len(X_copy[i,:])): for l in range(len(X_copy[i,k,:])): if np.isnan(X_copy[i,k,l]): X_copy[i,k,l] = X_copy[i,k-1,l] return(X_copy) #this is what I do in the actual code #X = np.load('C:/WinPython/WPy64-3830/data/X.npy') #Y = np.load('C:/WinPython/WPy64-3830/scripts/Y.npy') #simulated junk data X, Y = create_x() #create a dense matrix from the sparse one. X = densify_np(X) seed = 7 np.random.seed(seed) array_size = 64 X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=seed) batch = 64 model = Sequential() model.add(layers.Input(shape=(500,array_size))) model.add(layers.Masking(mask_value=0.,input_shape=(500, array_size))) model.add(BatchNormalization()) model.add(layers.SimpleRNN(1, activation=None, dropout = 0, recurrent_dropout=.2)) model.add(layers.Dense(1, activation = 'sigmoid')) opt = K.optimizers.Adam(learning_rate=.001) model.compile(loss='binary_crossentropy', optimizer=opt) model.fit(X_train, y_train.astype(int), validation_data=(X_test,y_test.astype(int)), epochs=25, batch_size=batch) explainer = shap.DeepExplainer(model, X_test) shap_values = explainer.shap_values(X_train) Running the last line to create the shap_values yields the error below.
StagingError Traceback (most recent call last) <ipython-input-6-f789203da9c8> in <module> 1 import shap 2 explainer = shap.DeepExplainer(model, X_test) ----> 3 shap_values = explainer.shap_values(X_train) 4 print('done') C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\shap\explainers\deep\__init__.py in shap_values(self, X, ranked_outputs, output_rank_order, check_additivity) 117 were chosen as "top". 118 """ --> 119 return self.explainer.shap_values(X, ranked_outputs, output_rank_order, check_additivity=check_additivity) C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\shap\explainers\deep\deep_tf.py in shap_values(self, X, ranked_outputs, output_rank_order, check_additivity) 302 # run attribution computation graph 303 feature_ind = model_output_ranks[j,i] --> 304 sample_phis = self.run(self.phi_symbolic(feature_ind), self.model_inputs, joint_input) 305 306 # assign the attributions to the right part of the output arrays C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\shap\explainers\deep\deep_tf.py in run(self, out, model_inputs, X) 359 360 return final_out --> 361 return self.execute_with_overridden_gradients(anon) 362 363 def custom_grad(self, op, *grads): C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\shap\explainers\deep\deep_tf.py in execute_with_overridden_gradients(self, f) 395 # define the computation graph for the attribution values using a custom gradient-like computation 396 try: --> 397 out = f() 398 finally: 399 # reinstate the backpropagatable check C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\shap\explainers\deep\deep_tf.py in anon() 355 v = tf.constant(data, dtype=self.model_inputs[i].dtype) 356 inputs.append(v) --> 357 final_out = out(inputs) 358 tf_execute.record_gradient = tf_backprop._record_gradient 359 C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds) 778 else: 779 compiler = "nonXla" --> 780 result = self._call(*args, **kwds) 781 782 new_tracing_count = self._get_tracing_count() C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds) 821 # This is the first call of __call__, so we have to initialize. 822 initializers = [] --> 823 self._initialize(args, kwds, add_initializers_to=initializers) 824 finally: 825 # At this point we know that the initialization is complete (or less C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to) 694 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph) 695 self._concrete_stateful_fn = ( --> 696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access 697 *args, **kwds)) 698 C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs) 2853 args, kwargs = None, None 2854 with self._lock: -> 2855 graph_function, _, _ = self._maybe_define_function(args, kwargs) 2856 return graph_function 2857 C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs) 3211 3212 self._function_cache.missed.add(call_context_key) -> 3213 graph_function = self._create_graph_function(args, kwargs) 3214 self._function_cache.primary[cache_key] = graph_function 3215 return graph_function, args, kwargs C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) 3063 arg_names = base_arg_names + missing_arg_names 3064 graph_function = ConcreteFunction( -> 3065 func_graph_module.func_graph_from_py_func( 3066 self._name, 3067 self._python_function, C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes) 984 _, original_func = tf_decorator.unwrap(python_func) 985 --> 986 func_outputs = python_func(*func_args, **func_kwargs) 987 988 # invariant: `func_outputs` contains only Tensors, CompositeTensors, C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds) 598 # __wrapped__ allows AutoGraph to swap in a converted function. We give 599 # the function a weak reference to itself to avoid a reference cycle. --> 600 return weak_wrapped_fn().__wrapped__(*args, **kwds) 601 weak_wrapped_fn = weakref.ref(wrapped_fn) 602 C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs) 971 except Exception as e: # pylint:disable=broad-except 972 if hasattr(e, "ag_error_metadata"): --> 973 raise e.ag_error_metadata.to_exception(e) 974 else: 975 raise StagingError: in user code: C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\shap\explainers\deep\deep_tf.py:244 grad_graph * x_grad = tape.gradient(out, shap_rAnD) C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\eager\backprop.py:1067 gradient ** flat_grad = imperative_grad.imperative_grad( C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\eager\imperative_grad.py:71 imperative_grad return pywrap_tfe.TFE_Py_TapeGradient( C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\eager\backprop.py:151 _gradient_function grad_fn = ops._gradient_registry.lookup(op_name) # pylint: disable=protected-access C:\WinPython\WPy64-3830\python-3.8.3.amd64\lib\site-packages\tensorflow\python\framework\registry.py:96 lookup raise LookupError( LookupError: gradient registry has no entry for: shap_TensorListStack 
